most citedPOPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference

4 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20241 cited

Real3D: Scaling Up Large Reconstruction Models with Real-World Images

Hanwen Jiang, Qixing Huang, Georgios Pavlakos

The default strategy for training single-view Large Reconstruction Models (LRMs) follows the fully supervised route using large-scale datasets of synthetic 3D assets or multi-view…

cs.CV2024

CoFie: Learning Compact Neural Surface Representations with Coordinate Fields

Hanwen Jiang, Haitao Yang, Georgios Pavlakos +1

This paper introduces CoFie, a novel local geometry-aware neural surface representation. CoFie is motivated by the theoretical analysis of local SDFs with quadratic approximation.…

cs.CV20231 cited

LEAP: Liberate Sparse-view 3D Modeling from Camera Poses

Hanwen Jiang, Zhenyu Jiang, Yue Zhao +1

Are camera poses necessary for multi-view 3D modeling? Existing approaches predominantly assume access to accurate camera poses. While this assumption might hold for dense views, a…

cs.CV2023

Doduo: Learning Dense Visual Correspondence from Unsupervised Semantic-Aware Flow

Zhenyu Jiang, Hanwen Jiang, Yuke Zhu

Dense visual correspondence plays a vital role in robotic perception. This work focuses on establishing the dense correspondence between a pair of images that captures dynamic scen…

cs.CV20234 cited

POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference

Zhiwen Fan, Panwang Pan, Peihao Wang +4

Despite the significant progress in six degrees-of-freedom (6DoF) object pose estimation, existing methods have limited applicability in real-world scenarios involving embodied age…